OP0127 HARNESSING THE POWER OF RADIOMICS TO PREDICT SURVIVAL IN PATIENTS WITH RA-ILD
Authors: V. Venerito, A. Manfredi, G. Lopalco, M. Lavista, G. Cassone, A. Scardapane, M. Sebastiani, F. Iannone
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Patients with rheumatoid arthritis (RA) and interstitial lung disease (ILD) have increased mortality compared to the general population and factors capable of predicting RA-ILD long-term clinical outcomes are lacking. In oncology, radiomics allows the quantification of tumour phenotype by analysing the characteristics of medical images [2]. The hypothesis of radiomics is that the distinctive imaging features between disease forms may help make a prognosis and predict the therapeutic response for various conditions, thus providing valuable information for personalised therapy and patient management. Using specific software, it is possible to segment organs on high-resolution computed tomography (HRCT) images and extract many features that may uncover disease characteristics that are not detected by the naked eye. <h3>Objectives</h3> We aimed to investigate whether features from whole lung radiomic analysis of HRCT may. alone predict mortality in RA-ILD patients. <h3>Methods</h3> HRCTs of RA patients from January 2012 to March 2022 were analyzed. The time between the first available HRCT and the last follow-up visit or ILD-related death was recorded. We performed a volumetric analysis in 3D Slicer, automatically segmenting the whole lungs and trachea via the Lung CT Analyzer. The PyRadiomics platform (version 3.01) can extract radiomic data from medical imaging, calculate features and return results as continuous variables. In a Python 3.9 environment, we extracted 120 features for segmented regions. A LASSO-Cox model was carried out by considering ILD-related death as the outcome variable and extracting radiomic features as exposure variables. <h3>Results</h3> We retrieved the HRCTs of 30 RA-ILD patients. The median survival time (interquartile range) was 48 months (36–120 months). Thirteen out of 30 (43.33%) patients died during the observation period. Demographics and clinical features have been reported in Table 1. Whole line segmentation was fast and reliable. The model included either the median grey level intensity within the whole lung segmentation (HR 9.35, 95% CI 1.56–v55.86) as a positive predictor of death and the 10th percentile of the number of included voxels (HR 0.20, 95% CI 0.05–0.84), the voxel-based pre-processing information (HR 0.23, 95% CI 0.06–0.82) and the flatness (HR 0.42, 95% CI 0.18–0.98), negatively correlating to mortality. The correlation of grey level values to their respective voxels (HR 1.52 95% CI 0.82–2.83) was also retained as a confounder. We observed that the hazard function followed the 45-degree line very closely except for huge values of time, demonstrating goodness-of-fit (Figure 2, Panel. B). <h3>Conclusion</h3> Radiomics may predict RA-ILD patients' mortality and may promote HRCT as a digital biomarker regardless of the clinical characteristics of the disease. <h3>References</h3> [1]Huang S, Kronzer VL, Dellaripa PF, Deane KD, Bolster MB, Nagaraja V, et al. Rheumatoid arthritis-associated interstitial lung disease: current update on prevalence, risk factors, and pharmacologic treatment. Curr Treatm Opt Rheumatol. (2020) 6:337–53. doi: 10.1007/s40674-020-00160-z [2]van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. (2017) 77:e104–7. doi: 10.1158/0008-5472.CAN-17-0339 <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> None Declared.